Emerging Technology
Special Interest Group
Aug 10th, 2023
Please add your aendance to:
hps://github.com/nos/zenith/issues/69
Agenda
▪Announcements
▪Deep Dives
▪Arcial Intelligence Primers
▪POC Program
▪Any Other Admin
▪Call to Acon
▪Any Other Business
▪Thanks & Close-Out
Announcements
Announcements
Upcoming Events
Thursday, Aug 10 (Today!)
▪11am EST / 4pm BST
FDC3: Web Browsers
▪11pm EST / 4pm BST
Morphir
hps://www.nos.org/news-and-events
▪September 19 - Linux Foundaon Open
Source Summit Europe
Open Source Summit is the premier event for open source
developers, technologists, and community leaders to collaborate,
share informaon, solve problems, and gain knowledge,
furthering open source innovaon and ensuring a sustainable
open source ecosystem. It is the gathering place for open-source
code and community contributors. Register here.
▪November 1 –
Open Source in Finance Forum - NYC
Registraon is open for our annual Open Source in Finance
Forum in the Marrio Marquis Hotel in Times Square NYC. Find
informaon on how to sponsor or register here.
Announcements
Upcoming Events
All through August
🏆 Prizes:
Exclusive Interview:
There's a chance to get interviewed by Sal
Kimmich, a known leader in Open Source, AI and
DevOps, working with site reliability engineers
and cybersecurity specialists to implement best
tools and practices to remove toil from developer
workflows.
Showcase Your Ideas:
The top entries will be featured in Gadfly AI's
Cyberscape Zine, giving you exposure in the AI
and tech community!
How to Parcipate:
1. Write a story about AI on Hackernoon.com.
2. Add #future-of-AI to your entry.
3. Share your thoughts on the future of AI.
🚀 Topics to Explore:
▪How AI is changing creavity and art.
▪The blend of AI and generave art + code.
▪Ethical and regulatory aspects of AI.
▪Latest AI research insights.
▪Embrace the transformave power of AI.
▪Dive into AI security discussions.
Sal Kimmich
Director of
Open Source,
Escher Cloud
AI Chipsets
Specialized processors
designed to accelerate AI
computations, enabling
faster and more efficient AI
model training and inference.
In fintech, AI chipsets drive
groundbreaking
advancements, powering
complex algorithms for fraud
detection, risk assessment,
and personalized financial
recommendations.
The high-performance
computing capabilities of AI
chipsets empower fintech
companies to deliver real-
time, data-intensive services,
transforming the way
financial institutions operate
and serve their customers.
You can find out more about
this subject in our AI Chipset
Primer on the Zenith GitHub.
8
Surging Demand
for applications
Deep learning
advancements
Need for
fast/ecient data
processing
Regulatory pressure to
improve fraud detecon
Cost savings through AI-
driven automation
Integraon across mulple
processes
AI-powered data analytics
Desire to enhance financial
inclusivity & accessibility
Legacy hardware
manufacturers
Tradional nancial services
requiring adaptaon
Skill shis in job market as AI
impacts workforce
Faster & more accurate decision making
Risk mgmt. & fraud detection capabilities
Real-me data processing for insights
Optimised AI model training on chipsets
AI-powers Fintech expands market reach
Increased investment for innovative startups
Data storage and integraon chipsets
Collaboraon with data/chipset manufacturers
Disrupon to trad. hardware supply chains
Success of
accelerated AI
model training
Scalability of AI
chipsets for high
volume data
Need for continuous upskilling to ensure
workforce proficiency
Growing ecosystem
of service providers
Availability of dev
tools & frameworks
Need for connuous upskilling to ensure
chipsets are implemented correctly
Data privacy
concerns
Integraon
challenges with
legacy hardware
Regulatory compliance complexities
Short term
Inial adopon and
integraon of chipsets
Medium term
Widespread deployment
in financial services
Long term
Evolution of chipsets for
autonomous solutions
Potential delays in
advancements and
innovations
Market resistance and
scepcism in AI-decision
making
Ethical consideraons
surrounding responsible
use
AI chipsets oer a compelling opportunity for ntech, revoluonizing nancial services through accelerated AI computaons. Advancements in specialized hardware enable groundbreaking
applicaons in fraud detecon, risk assessment, and personalized nancial recommendaons. The high-performance compung capabilies of AI chipsets empower real-me, data-intensive
nancial services, transforming tradional banking and ushering in a new era of customer-centric nancial experiences. Despite technical feasibility and potenal nancial benets, challenges like
data privacy, regulatory compliance, and integraon fricons require careful navigaon. With a promising future outlook, early adopon and collaboraon between ntech players and AI chipset
manufacturers will play a crucial role in shaping the trajectory of AI-driven ntech innovaon.
AI-Driven
Fraud Detection
This leverages advanced
machine learning algorithms
to detect and prevent
fraudulent activities in real-
time.
In fintech, this technology
acts as a vigilant security
layer, continuously analysing
vast volumes of financial
data to identify suspicious
patterns and transactions.
By swiftly detecting and
mitigating fraud, AI-driven
systems protect financial
assets, preserve customer
trust, and enhance overall
cybersecurity in the rapidly
evolving digital financial
landscape.
You can find out more about
this subject in our AI Chipset
Primer on the Zenith GitHub.
5
Escalating cyber
threats
AI advancements
increase accuracy
& efficiency
Growing adoption
of digital financial
services
Regulatory mandates &
compliance reqs.
Signicant cost
saving through
loss prevenon
Brand reputaon
& Consumer trust
Widespread adoption leads
to collective intelligence
Collaborave Industry-wide
defence strategy
Increased trust in FS
promotes further adoption
Cybercriminals facing more
evolved systems
Legacy fraud detection
losing competitiveness
Shi in the dynamics of
nancial crime invesgaon
toward automaon
Superior fraud detecon & reduced losses
Enhanced customer trust & loyalty
Real-time response capabilities to incidents
Cost savings & less post-fraud invesgaons
Customer retention through reputation
Investments & partnerships for potential
applications following successful tests
Integration of AI-driven solutions to core infra
Collaboraon between AI soluon providers,
data aggregators & nancial enes
Potenal changes in risk assessment and
underwring of Financial Services
Demonstrable
success in real world
scenarios
Scalability of AI
models to process
vast volumes of data
Connuous improvement and renement of ML
models to adapt to evolving threat landscape
Readily available
dev. frameworks
Growing ecosystem
of talent working
with AI tools
Need for continuous upskilling to keep up with
emerging fraud techniques
Data privacy
concerns
Over-reliance on AI
models leading to
blind spots
Regulatory compliance complexies in
explainability of models
Short term
Integration efforts into
security infrastructure
Medium term
Industry-wide adopon &
increased reliance
Long term
Evolution of AI-driven
responses to emerging
fraud techniques
False negatives where AI
fails to detect new or
adaptive fraud patterns
Adversarial attacks
targeting AI models to
manipulate outcomes
Potenal overng or
bias in AI models
aecng accuracy and/or
fairness of detecon
AI-driven fraud detecon represents a signicant opportunity in ntech, providing real-me protecon against sophiscated cyber threats. Advanced machine learning algorithms analyse vast
nancial data to swily idenfy and prevent fraudulent acvies, safeguarding nancial assets and customer trust. Collaboraon between ntech companies, nancial instuons, and
cybersecurity experts is driving the development and adopon of robust fraud prevenon soluons. The technology's impact is far-reaching, with macro network eects and improved
cybersecurity across the digital nancial landscape. While the potenal for nancial benets and compeve advantages is substanal, the implementaon of AI-driven fraud detecon requires
careful consideraon of technical feasibility, fricons, and risks. By striking the right balance between innovaon and responsible use, AI-driven fraud detecon will connue to transform the way
nancial enes combat nancial crime, contribung to a more secure and trusted nancial ecosystem.
Computer Vision
An AI technology that
enables machines to
interpret and understand
visual informaon.
In ntech, computer vision
revoluonizes various
processes, from automang
document vericaon and
identy recognion to
analysing nancial charts and
visualizing data paerns.
By harnessing the power of
computer vision, ntech
companies streamline
operaons, enhance user
experiences, and unlock
valuable insights from visual
data, driving eciency and
innovaon.
You can nd out more about
this subject in our AI Chipset
Primer on the Zenith GitHub.
Increasing
availability of HQ
visual data
Growing demand
for automaon &
eciency
Rising interest in
data-driven FS
decision making
Competitive advantage
through visualising
complex data
Potential
improvements to
fraud detection
Need for fast &
accurate ID & docs
vericaon
More focus on visual
interacon with data
Combinaon of computer
vision with other AI tech
Improved accessibility &
inclusivity via automation
Obsolescence creates need
to adapt to automaon
Conventional data analysis
methods challenged
Shift in the skill set required
within FS Operational tasks
Quick & seamless document processing
Enhanced security through adv. vericaon
Data-driven decision making for strategy
Automaon of labour intensive doc vericaon
Higher customer engagement & satisfaction
Demonstraon of technological leadership &
innovaon of products with customer focus
Integration of end-to-end operations
Collaboration required to meet specific
industry needs
Streamlining of data collection & analysis
Real-world demos of
visual data
interpretaon
Scalability to handle
large-scale real-me
data
Connuous improvement and renement
required to improve accuracy & eciency
Readily available
libraries, APIs and
tools
Growing ecosystem
of collaborang
enes
Need for continuous upskilling to keep up with
new data use cases
Data privacy
concerns over
processing
Ethical
considerations when
decision making
Potenal biases aecng nancial data analysis
& decision outcomes
Short term
Document vericaon
and basic data analysis
Medium term
Widespread POCs for
other FS operaons
Long term
Augmented Reality based
financial interactions
Technical challenges
aecng decision making
Regulatory compliance in
ID verification &
customer data processing
Market resistance to
automated interpretation
of visual data
Computer vision technology holds immense potential in transforming fintech processes, enabling machines to interpret and understand visual information. Fintech companies are leveraging
computer vision to automate document verification, enhance fraud detection, and gain valuable insights from complex financial data. The technology's impact extends to improved user
experiences, data-driven decision-making, and increased efficiency in financial operations. While the opportunity is significant, challenges related to technical feasibility, ethical considerations,
and regulatory compliance must be addressed. The future outlook for computer vision in fintech is promising, with a gradual timeline for adoption and a need for careful risk management and
responsible implementation. By capitalizing on the benefits and addressing frictions, computer vision will play a pivotal role in driving innovation and efficiency in the evolving landscape of
fintech services.
9
Proof of Concepts
Test out new technologies
Leo
Mordasini
POC Program
Co-ordinator
Exploraon program within the SIG aiming to test concepts and
create new projects
Enables innovators to be able to pursue conceptual designs and ideas
with the proper resources and sponsorship
Control gates on the process to manage the flow of funding and duration
of exploration so that we can fail fast and win quickly.
Projects subject to a vetting process
What is a Proof of Concept (PoC)
Phase 1: Ideaon
▪Crowdsource for ideas on how to solve the biggest blockers with our primers
Phase 2: Proposal
▪Aer gathering feedback from the open-source community, ocially pitch your idea
for approval, funding and sponsorship
Phase 3: POC Kicko
▪Provide the environment and tools needed to successfully facilitate exploraon of
ideas
Phase 4: Demo
▪Showcase wins/loses and contribute insights back to the community
POC Process Overview
Ideation Proposal Kickoff Demo
Phase 1:
Ideation
Opportunity Outline
What is a Primer
▪Created by subject maer experts within
the Brain Trust
▪Provides an introducon to the subject &
relevant reading to seed knowledge
▪Open for comments for further discussion &
follow-ups
Outline where disrupon is possible or
worth exploring
▪Current blockers are called out to aid exploraon
in our POC program
Ideaon Proposal Kicko Demo
Call for Action –Discuss your ideas!
▪Opportunity is posted in
the Zenith repo when
available
▪Utilize GitHub Discussions
to facilitate ideation
amongst the community
Ideaon Proposal Kicko Demo
Submission of Idea
Innovators submit a PR to the Zenith GitHub
repository in the respective opportunity folder
▪Pull Request is the submission of the idea, based
on a PR template provided
Template will include all the questions the
submitter needs to answer as part of the idea
submission
Ideation Proposal Kicko Demo
SME Veng & Feedback
Sanity check by the Brain Trust to confirm
ideas are:
▪Possible
▪Feasible
▪Worthwhile
▪Legal
▪Not already explored
At least 3 approvals from the brain trust for an
idea to move to the value proposition phase
Fully automated via GitHub to not add
unnecessary steps and ensure a seamless
onboarding process
Ideaon Proposal Kickoff Demo
Phase 2:
Proposal
Pitch Value Proposion
Partner with the Brain Trust
•You will be partnered with a mentor from the
Brain Trust who will help your team put together
the pitch value proposion
Create a video proposal
•Your team will have the opportunity to create a
proposal for project approval, funding and
requesng addional resources.
•This approach removes FINOS from being a
boleneck when reviewing ideas
Ideation Proposal Kickoff Demo
Funding Governance
A pool of representaves from FINOS
vote to approve access to exploraon
funds or resources to facilitate
exploraon
Funding would have a per-project cap
on both budget and duraon
Projects without funding asks can skip
this step
Ideaon Proposal Kickoff Demo
Further phases to
be covered in a
future session
Call to Action!
First three deep dives have been published!
▪AI Chipsets
▪AI-Driven Fraud Detecon
▪Computer Vision
Ideaon Proposal Kickoff Demo
Ideation Proposal Kickoff Demo
Start collaborang in Discussions!
▪Ulize Discussion tools to
collaborate with the open source
community:
▪General Discussion
▪Ideas
▪Polls
▪Q&A
▪Show and Tell
Any Other Business?
